In the 21st century, cars are an important mode of transportation that provides us the opportunity for personal control and autonomy. In day-to-day life, people use cars for commuting to work, shopping, visiting family and friends, etc. Research shows that more than 76% of people prevent themselves from traveling somewhere if they don't have a car. Most people tend to buy different types of cars based on their day-to-day necessities and preferences. So, it is essential for automobile companies to analyze the preference of their customers before launching a car model into the market. Austo, a UK-based automobile company aspires to grow its business into the US market after successfully establishing its footprints in the European market.

In order to be familiar with the types of cars preferred by the customers and factors influencing the car purchase behavior in the US market, Austo has contracted a consulting firm. Based on various market surveys, the consulting firm has created a dataset of 3 major types of cars that are extensively used across the US market. They have collected various details of the car owners which can be analyzed to understand the automobile market of the US.

Objective

Austo’s management team wants to understand the demand of the buyers and trends in the US market. They want to build customer profiles based on the analysis to identify new purchase opportunities so that they can manipulate the business strategy and production to meet certain demand levels. Further, the analysis will be a good way for management to understand the dynamics of a new market. Suppose you are a Data Scientist working at the consulting firm that has been contracted by Austo. You are given the task to create buyer’s profiles for different types of cars with the available data as well as a set of recommendations for Austo. Perform the data analysis to generate useful insights that will help the automobile company to grow its business.

Data Description

austo_automobile.csv: The dataset contains buyer’s data corresponding to different types of products(cars).

Data Dictionary

Age: Age of the customer Gender: Gender of the customer Profession: Indicates whether the customer is a salaried or business person Marital_status: Marital status of the customer Education: Refers to the highest level of education completed by the customer No_of_dependents: Number of dependents(partner/children/spouse) of the customer Personal_loan: Indicates whether the customer availed a personal loan or not House_loan: Indicates whether the customer availed house loan or not Partner_working: Indicates whether the customer's partner is working or not Salary: Annual Salary of the customer Partner_salary: Annual Salary of the customer's partner Total_salary: Annual household income (Salary + Partner_salary) of the customer's family Price: Price of the car Make: Car type (Hatchback/Sedan/SUV)

import os
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
sns.set (color_codes=True)
%matplotlib inline


import warnings
warnings.filterwarnings("ignore")

data = pd.read_csv ("Downloads/download-3.csv")

df = data.copy()

df.head()

	Age	Gender	Profession	Marital_status	Education	No_of_Dependents	Personal_loan	House_loan	Partner_working	Salary	Partner_salary	Total_salary	Price	Make
0	24	Male	Salaried	Married	Post Graduate	4	No	Yes	Yes	52000	25000	77000	18000	Hatchback
1	28	Male	Salaried	Married	Post Graduate	3	No	Yes	No	68000	0	68000	18000	Hatchback
2	23	Male	Salaried	Married	Graduate	4	Yes	Yes	No	31000	0	31000	18000	Hatchback
3	26	Male	Business	Married	Post Graduate	4	Yes	Yes	Yes	66000	35000	101000	18000	Hatchback
4	28	Male	Business	Married	Post Graduate	4	Yes	No	No	64000	0	64000	18000	Hatchback

df.head (50)

	Age	Gender	Profession	Marital_status	Education	No_of_Dependents	Personal_loan	House_loan	Partner_working	Salary	Partner_salary	Total_salary	Price	Make
0	24	Male	Salaried	Married	Post Graduate	4	No	Yes	Yes	52000	25000	77000	18000	Hatchback
1	28	Male	Salaried	Married	Post Graduate	3	No	Yes	No	68000	0	68000	18000	Hatchback
2	23	Male	Salaried	Married	Graduate	4	Yes	Yes	No	31000	0	31000	18000	Hatchback
3	26	Male	Business	Married	Post Graduate	4	Yes	Yes	Yes	66000	35000	101000	18000	Hatchback
4	28	Male	Business	Married	Post Graduate	4	Yes	No	No	64000	0	64000	18000	Hatchback
5	36	Male	Salaried	Married	Post Graduate	3	Yes	No	No	66000	0	66000	49000	SUV
6	28	Male	Business	Married	Graduate	4	Yes	No	Yes	48000	40000	88000	18000	Hatchback
7	27	Male	Salaried	Married	Post Graduate	3	No	No	Yes	76000	35000	111000	18000	Hatchback
8	46	Female	Salaried	Single	Post Graduate	0	Yes	No	No	80000	0	80000	49000	SUV
9	24	Male	Salaried	Single	Graduate	2	No	Yes	No	30000	0	30000	18000	Hatchback
10	46	Female	Salaried	Married	Graduate	4	No	No	No	70000	0	70000	49000	SUV
11	26	Male	Business	Married	Post Graduate	4	No	No	No	67000	0	67000	18000	Hatchback
12	23	Male	Salaried	Married	Post Graduate	3	Yes	No	No	52000	0	52000	18000	Hatchback
13	44	Female	Business	Single	Post Graduate	2	No	No	No	84000	0	84000	31000	Sedan
14	35	Male	Business	Married	Post Graduate	2	Yes	Yes	Yes	61000	35000	96000	31000	Sedan
15	29	Male	Salaried	Married	Post Graduate	4	No	No	No	76000	0	76000	18000	Hatchback
16	32	Male	Salaried	Married	Post Graduate	1	Yes	No	Yes	56000	35000	91000	31000	Sedan
17	23	Male	Salaried	Married	Post Graduate	3	Yes	Yes	No	58000	0	58000	18000	Hatchback
18	30	Male	Business	Married	Post Graduate	4	No	No	Yes	67000	22000	89000	18000	Hatchback
19	27	Male	Business	Married	Post Graduate	3	Yes	No	Yes	65000	35000	100000	18000	Hatchback
20	39	Female	Business	Married	Post Graduate	4	No	No	Yes	72000	50000	122000	49000	SUV
21	22	Male	Salaried	Married	Post Graduate	3	Yes	No	No	53000	0	53000	18000	Hatchback
22	40	Male	Business	Married	Graduate	1	No	Yes	No	59000	0	59000	31000	Sedan
23	47	Male	Salaried	Single	Post Graduate	2	Yes	No	No	87000	0	87000	49000	SUV
24	53	Male	Salaried	Married	Graduate	4	Yes	No	Yes	80000	50000	130000	49000	SUV
25	30	Male	Business	Married	Graduate	2	No	No	Yes	42000	26000	68000	18000	Hatchback
26	22	Male	Salaried	Married	Graduate	2	No	No	Yes	39000	35000	74000	18000	Hatchback
27	28	Female	Salaried	Married	Post Graduate	2	Yes	No	No	60000	0	60000	31000	Sedan
28	58	Male	Salaried	Married	Post Graduate	3	No	No	Yes	87000	26000	113000	50000	SUV
29	42	Female	Business	Married	Graduate	2	No	No	Yes	54000	35000	89000	31000	Sedan
30	24	Male	Business	Married	Post Graduate	3	No	Yes	Yes	57000	40000	97000	18000	Hatchback
31	28	Female	Salaried	Married	Graduate	1	Yes	No	Yes	36000	27000	63000	31000	Sedan
32	26	Male	Business	Married	Post Graduate	3	No	No	Yes	69000	40000	109000	18000	Hatchback
33	44	Male	Salaried	Single	Post Graduate	0	Yes	No	No	68000	0	68000	50000	SUV
34	24	Male	Salaried	Married	Post Graduate	3	Yes	No	Yes	60000	40000	100000	18000	Hatchback
35	58	Male	Business	Married	Post Graduate	3	Yes	No	Yes	78000	40000	118000	50000	SUV
36	26	Male	Business	Married	Graduate	3	No	No	No	51000	0	51000	18000	Hatchback
37	27	Male	Salaried	Married	Graduate	2	No	No	Yes	49000	26000	75000	18000	Hatchback
38	25	Male	Salaried	Married	Post Graduate	2	No	Yes	Yes	54000	50000	104000	18000	Hatchback
39	27	Male	Salaried	Married	Post Graduate	3	Yes	No	Yes	71000	25000	96000	18000	Hatchback
40	29	Female	Salaried	Single	Graduate	2	No	Yes	No	37000	0	37000	31000	Sedan
41	27	Male	Business	Married	Post Graduate	3	No	No	Yes	63000	40000	103000	18000	Hatchback
42	29	Male	Salaried	Married	Graduate	3	No	No	No	57000	0	57000	18000	Hatchback
43	28	Male	Business	Married	Graduate	2	Yes	No	Yes	43000	25000	68000	18000	Hatchback
44	56	Male	Salaried	Married	Graduate	3	Yes	No	Yes	72000	50000	122000	50000	SUV
45	28	Male	Salaried	Married	Post Graduate	3	Yes	Yes	Yes	60000	25000	85000	18000	Hatchback
46	38	Male	Business	Married	Graduate	2	Yes	Yes	No	65000	0	65000	31000	Sedan
47	42	Male	Salaried	Married	Post Graduate	3	No	No	Yes	74000	50000	124000	50000	SUV
48	43	Male	Salaried	Married	Post Graduate	2	No	No	Yes	75000	30000	105000	31000	Sedan
49	37	Male	Salaried	Married	Post Graduate	3	Yes	No	No	73000	0	73000	50000	SUV

Observations 1.Age range for the data set varies. 2.Profession in the data set is only salaried and business. 3.Some have partners that are unemployed.

data.shape

(1581, 14)

Comment

The data has 1581 rows and 14 columns.

data.dtypes. value_counts()

object    8
int64     6
dtype: int64

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1581 entries, 0 to 1580
Data columns (total 14 columns):
 #   Column            Non-Null Count  Dtype 
---  ------            --------------  ----- 
 0   Age               1581 non-null   int64 
 1   Gender            1581 non-null   object
 2   Profession        1581 non-null   object
 3   Marital_status    1581 non-null   object
 4   Education         1581 non-null   object
 5   No_of_Dependents  1581 non-null   int64 
 6   Personal_loan     1581 non-null   object
 7   House_loan        1581 non-null   object
 8   Partner_working   1581 non-null   object
 9   Salary            1581 non-null   int64 
 10  Partner_salary    1581 non-null   int64 
 11  Total_salary      1581 non-null   int64 
 12  Price             1581 non-null   int64 
 13  Make              1581 non-null   object
dtypes: int64(6), object(8)
memory usage: 173.0+ KB

Observations

All column have 1,581 each. The data contians intger and object data-type.

df['Gender'] = df.Gender.astype('category')
df['Profession'] = df.Profession.astype('category')
df['Marital_status'] = df.Marital_status.astype('category')
df['Education'] = df.Education.astype ('category')
df['Personal_loan'] = df.Personal_loan.astype('category')
df['House_loan'] = df.House_loan.astype('category')
df['Partner_working'] = df.Partner_working.astype('category')
df['Make'] = df.Make.astype('category')

# Converting categorical column to categorical type 

df.dtypes

Age                    int64
Gender              category
Profession          category
Marital_status      category
Education           category
No_of_Dependents       int64
Personal_loan       category
House_loan          category
Partner_working     category
Salary                 int64
Partner_salary         int64
Total_salary           int64
Price                  int64
Make                category
dtype: object

df.describe().T

	count	mean	std	min	25%	50%	75%	max
Age	1581.0	32.211259	9.125477	22.0	25.0	29.0	38.0	60.0
No_of_Dependents	1581.0	2.457938	0.943483	0.0	2.0	2.0	3.0	4.0
Salary	1581.0	59732.447818	14278.642665	30000.0	51000.0	59000.0	71000.0	90000.0
Partner_salary	1581.0	19720.430108	19480.214404	0.0	0.0	25000.0	38000.0	80000.0
Total_salary	1581.0	79452.877925	24855.936043	30000.0	61000.0	78000.0	96000.0	158000.0
Price	1581.0	35597.722960	13633.636545	18000.0	25000.0	31000.0	47000.0	70000.0

df.describe(exclude='number').T

	count	unique	top	freq
Gender	1581	2	Male	1252
Profession	1581	2	Salaried	896
Marital_status	1581	2	Married	1443
Education	1581	2	Post Graduate	985
Personal_loan	1581	2	Yes	792
House_loan	1581	2	No	1054
Partner_working	1581	2	Yes	868
Make	1581	3	Hatchback	884

cat_col=['Gender', 'Profession', 'Marital_status', 'Education', 'Personal_loan', 'House_loan', 'Partner_working', 'Make']

for column in cat_col:
    print(df[column].value_counts())
    print('-'*50)

Male      1252
Female     329
Name: Gender, dtype: int64
--------------------------------------------------
Salaried    896
Business    685
Name: Profession, dtype: int64
--------------------------------------------------
Married    1443
Single      138
Name: Marital_status, dtype: int64
--------------------------------------------------
Post Graduate    985
Graduate         596
Name: Education, dtype: int64
--------------------------------------------------
Yes    792
No     789
Name: Personal_loan, dtype: int64
--------------------------------------------------
No     1054
Yes     527
Name: House_loan, dtype: int64
--------------------------------------------------
Yes    868
No     713
Name: Partner_working, dtype: int64
--------------------------------------------------
Hatchback    884
Sedan        460
SUV          237
Name: Make, dtype: int64
--------------------------------------------------

Observations

1.Marrried, Educated Male that does not own a house bought most of the cars. 2.Majority of the car bought was hatcback 3.The median Age, No of dependents, salary, partner-salary, total salary, and price is 29, 2, 59,000, 25,000, 78,000, 31,000 respectively. 4.Max age is 60 and minimum 22 5.Max total salary is 158, 000 and minimum 30,000 6.The average price for a car is between 31,000 - 35,000.

df.isna().sum()

Age                 0
Gender              0
Profession          0
Marital_status      0
Education           0
No_of_Dependents    0
Personal_loan       0
House_loan          0
Partner_working     0
Salary              0
Partner_salary      0
Total_salary        0
Price               0
Make                0
dtype: int64

No missing value in the data set

Let's check the count of each unique category in each of the categorical variables.

def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
    """
    Boxplot and histogram combined

    data: dataframe
    feature: dataframe column
    figsize: size of figure (default (12,7))
    kde: whether to show the density curve (default False)
    bins: number of bins for histogram (default None)
    """
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2,  # Number of rows of the subplot grid= 2
        sharex=True,  # x-axis will be shared among all subplots
        gridspec_kw={"height_ratios": (0.25, 0.75)},
        figsize=figsize,
    )  # creating the 2 subplots
    sns.boxplot(
        data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
    )  # boxplot will be created and a star will indicate the mean value of the column
    sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
    ) if bins else sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2
    )  # For histogram
    ax_hist2.axvline(
        data[feature].mean(), color="green", linestyle="--"
    )  # Add mean to the histogram
    ax_hist2.axvline(
        data[feature].median(), color="black", linestyle="-"
    )  # Add median to the histogram

def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
   
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2, 
        sharex=True,  
        gridspec_kw={"height_ratios": (0.25, 0.75)},
        figsize=figsize,
    ) 
    sns.boxplot(
        data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
    )  
    sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
    ) if bins else sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2
    )  
    ax_hist2.axvline(
        data[feature].mean(), color="green", linestyle="--"
    )  
    ax_hist2.axvline(
        data[feature].median(), color="black", linestyle="-"
    )  

histogram_boxplot(df, 'Age')

df['Age']. mode()

0    28
dtype: int64

# Which age is paying the highest charges for car?
data[df['Age'] == df['Age'].max()]

	Age	Gender	Profession	Marital_status	Education	No_of_Dependents	Personal_loan	House_loan	Partner_working	Salary	Partner_salary	Total_salary	Price	Make
70	60	Female	Salaried	Married	Post Graduate	3	No	No	No	77000	0	77000	50000	SUV
201	60	Female	Salaried	Married	Post Graduate	3	Yes	No	Yes	80000	50000	130000	51000	SUV
470	60	Female	Salaried	Married	Post Graduate	3	No	No	Yes	84000	60000	144000	57000	SUV
502	60	Male	Salaried	Married	Post Graduate	3	No	No	Yes	79000	60000	139000	57000	SUV
514	60	Male	Salaried	Married	Post Graduate	3	No	No	No	83000	0	83000	57000	SUV
559	60	Female	Salaried	Married	Post Graduate	3	No	No	Yes	79000	60000	139000	57000	SUV
920	60	Male	Salaried	Married	Post Graduate	4	No	No	No	89000	0	89000	61000	SUV
935	60	Female	Salaried	Married	Graduate	2	Yes	No	Yes	72000	70000	142000	61000	SUV
943	60	Female	Salaried	Married	Post Graduate	4	Yes	No	Yes	82000	70000	152000	61000	SUV
972	60	Male	Business	Married	Post Graduate	4	No	No	Yes	86000	70000	156000	61000	SUV
977	60	Female	Salaried	Married	Graduate	3	Yes	No	No	77000	0	77000	62000	SUV
1107	60	Male	Salaried	Married	Post Graduate	2	No	No	No	81000	0	81000	63000	SUV
1320	60	Female	Business	Married	Graduate	3	No	No	No	74000	0	74000	66000	SUV
1336	60	Male	Salaried	Married	Post Graduate	4	Yes	No	No	81000	0	81000	67000	SUV
1450	60	Male	Salaried	Married	Graduate	4	No	No	Yes	79000	40000	119000	68000	SUV

Observation

1.Maximum age is 60, minimum is 22

2.The age distribution is skewened to the right

3.Majority of the age is middle aged

4.Median age is equal 29 but the mean is ~32

5.There are outliers in this variable.

6.Age 60 is buying the expensive car

histogram_boxplot(df, 'No_of_Dependents')

histogram_boxplot(df, 'Salary')

histogram_boxplot(df, 'Partner_salary')

histogram_boxplot(df, 'Total_salary')

Observations

1.Salary and Total-salary does not have any outliers.

2.The distribution for Salary and Total-salary is close to normal, suggesting possible correlation between the two variables.

3.There is no outlier for all of the salary variables except total salary.

4.Partner-salary is skewed to the right, Also there is possible correlation between Partner-salary and dependents because of the visual proportion.

histogram_boxplot(df, 'Price')

Observation

1.The highest price for car is 80,000 and the minimum 18,,000.

2.The skewness follows the proportion of the partner salary iNdicating correlation.

Let's explore the categorical variables now¶

def labeled_barplot(data, feature, perc=False, n=None):
   

    total = len(data[feature]) 
    count = data[feature].nunique()
    if n is None:
        plt.figure(figsize=(count + 1, 5))
    else:
        plt.figure(figsize=(n + 1, 5))

    plt.xticks(rotation=90, fontsize=15)
    ax = sns.countplot(
        data=data,
        x=feature,
        palette="Paired",
        order=data[feature].value_counts().index[:n].sort_values(),
    )

    for p in ax.patches:
        if perc == True:
            label = "{:.1f}%".format(
                100 * p.get_height() / total
            )  
        else:
            label = p.get_height()  

        x = p.get_x() + p.get_width() / 2  
        y = p.get_height()  

        ax.annotate(
            label,
            (x, y),
            ha="center",
            va="center",
            size=12,
            xytext=(0, 5),
            textcoords="offset points",
        ) 

    plt.show() 
    plt.figure(figsize=(10,7))

# Create a function that returns a Pie chart for categorical variable:
def pie_chart(x = 'smoker'):
    """
    Function creates a Pie chart for categorical variables.
    """
    fig, ax = plt.subplots(figsize=(8, 6), subplot_kw=dict(aspect="equal"))

    s = data.groupby(x).size()

    mydata_values = s.values.tolist()
    mydata_index = s.index.tolist()

    def func(pct, allvals):
        absolute = int(pct/100.*np.sum(allvals))
        return "{:.1f}%\n({:d})".format(pct, absolute)


    wedges, texts, autotexts = ax.pie(mydata_values, autopct=lambda pct: func(pct, mydata_values),
                                      textprops=dict(color="w"))

    ax.legend(wedges, mydata_index,
              title="Index",
              loc="center left",
              bbox_to_anchor=(1, 0, 0.5, 1))

    plt.setp(autotexts, size=12, weight="bold")

    ax.set_title(f'{x.capitalize()} Piechart')

    plt.show()
    
   

pie_chart('Gender')

Observations

1.Male is 79.2% of the sample population

2.Female is 20.8% of the sample population

pie_chart('Profession')

Observations

1.Salaried is 56.7% of the sample population

2.Female is 43.3% of the sample population

pie_chart('Marital_status')

Observations

1.Married has the highest popuolation with 91.3%.

2.Single population is 8.7%

pie_chart('Education')

pie_chart('Make')

Observations

1.Hatchback has the highest count with 884 (55.9%) of the sample population

2.Sedan is 460(29.1%)

3.SUV is 237(15.0%)

sns.countplot(x = 'Make', data = data)

<AxesSubplot:xlabel='Make', ylabel='count'>

# Dispersion in type of car
sns.catplot(x='Price',
             col='Make', 
             data=df,
             col_wrap=4,
             kind="violin")
plt.show()

# Dispersion around age
sns.catplot(x='Age',
             col='Make', 
             data=df,
             col_wrap=4,
             kind="violin")
plt.show()

# Dispersion Total-salary
sns.catplot(x='Total_salary',
             col='Make', 
             data=df,
             col_wrap=4,
             kind="violin")
plt.show()

# Dispersion around Make
sns.catplot(x='Salary',
             col='Make', 
             data=df,
             col_wrap=4,
             kind="violin")
plt.show()

# Check for correlation among numerical variables
num_var = ['Age', 'No_of_Dependents', 'Salary', 'Partner_salary', 'Total_salary', 'Price']

corr = df[num_var].corr()

# plot the heatmap

plt.figure(figsize=(15, 7))
sns.heatmap(corr, annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="Spectral", xticklabels=corr.columns, yticklabels=corr.columns)
plt.show()

sns.heatmap(df.corr(), annot=True)  # plot the correlation coefficients as a heatmap

<AxesSubplot:>

# Observations 1.As expected,Age shows high correlation with Price 2.No of dependents of course would be negatively correlated with Price because number of dependents is low in single households. 3.It is important to note that correlation does not imply causation. 4.There does not seem to be a strong relationship between Total-salary and Price.

sns.pairplot(data=df[num_var], diag_kind="kde")
plt.show()

A partcularly interesting relationship between Age, Income and Make of car can be seen in this graph

df.corr()   # displays the correlation between every possible pair of attributes as a dataframe

	Age	No_of_Dependents	Salary	Partner_salary	Total_salary	Price
Age	1.000000	-0.157163	0.606094	0.150202	0.465891	0.790466
No_of_Dependents	-0.157163	1.000000	-0.043847	0.144508	0.088067	-0.135839
Salary	0.606094	-0.043847	1.000000	0.061943	0.623003	0.393038
Partner_salary	0.150202	0.144508	0.061943	1.000000	0.819309	0.156154
Total_salary	0.465891	0.088067	0.623003	0.819309	1.000000	0.348164
Price	0.790466	-0.135839	0.393038	0.156154	0.348164	1.000000

plt.figure(figsize=(9, 9))
sns.histplot(data = df, x = 'Age', hue = 'Make')
plt.show()

plt.figure(figsize=(9, 9))
sns.histplot(data = df, x = 'Salary', hue = 'Make')
plt.show()

sns.scatterplot(df['Age'], df['Price'])  # Plots the scatter plot using two variables

<AxesSubplot:xlabel='Age', ylabel='Price'>

sns.scatterplot(df['Age'], df['Salary'])  # Plots the scatter plot using two variables

<AxesSubplot:xlabel='Age', ylabel='Salary'>

sns.scatterplot(df['Age'], df['Total_salary'])  # Plots the scatter plot using two variables

<AxesSubplot:xlabel='Age', ylabel='Total_salary'>

plt.figure(figsize=(15,7))           
sns.boxplot(df['Make'],df['Total_salary'])
plt.ylabel('Total_salary')
plt.xlabel('Make')
plt.show()

plt.figure(figsize=(15,7))           
sns.boxplot(df['Make'],df['Salary'])
plt.ylabel('Salary')
plt.xlabel('Make')
plt.show()

plt.figure(figsize=(15,7))           
sns.boxplot(df['Make'],df['Salary'])
plt.ylabel('Salary')
plt.xlabel('Make')
plt.show()

plt.figure(figsize=(15,7))           
sns.boxplot(df['Make'],df['Price'])
plt.ylabel('Price')
plt.xlabel('Make')
plt.show()

pd.crosstab(df['Make'],df['Gender']).plot(kind="bar", figsize=(8,10),
                 stacked=True)
plt.legend()
plt.show()

pd.crosstab(df['Make'],df['Marital_status']).plot(kind="bar", figsize=(8,10),
                 stacked=True)
plt.legend()
plt.show()

Comment

1.There is a clear difference the prices of the each Make.

2.SUV is the most expensive car

3.Hatcback is the least expensive

data.groupby(['No_of_Dependents']).agg('count')['Total_salary']

No_of_Dependents
0     20
1    229
2    557
3    557
4    218
Name: Total_salary, dtype: int64

In the dataset, approximately 84% (1332 / 1581) of the Total salary have 2 and more dependents .

data.groupby(['Make']).agg('count')['Total_salary']

Make
Hatchback    884
SUV          237
Sedan        460
Name: Total_salary, dtype: int64

Observation

1.Suv is the least car bought by total_salary,

2.This indicate household with two salary prefer Hatchback and Sedan

3.indicating more than one car in household, Since both husband and wife are working.

# let us try pandas-profiling now and see how does it simplifies the EDA
# !pip install pandas-profiling==2.8.0

from pandas_profiling import ProfileReport
# Use the original dataframe, so that original features are considered
prof = ProfileReport(df)
# to view report created by pandas profile
prof